endogenous-macrodynamics-in-algorithmic-recourse
Software and data underlying the publication: Endogenous Macrodynamics in Algorithmic Recourse
Description
Code and research results for SaTML 2023 research paper. Originally released here: https://github.com/pat-alt/endogenous-macrodynamics-in-algorithmic-recourse.
The research results include:
Folders with images that went into a) the body of the paper or b) the online companion.Folders with results (.jls; .csv) for different experiments: a) synthetic data; b) real-world data; and, c) mitigation strategies for both categories of datasets (see paper for details on experiments). Results for all categories are further grouped by dataset.For each dataset, results include: a) "experiment.jls" files that can be loaded into a Julia session: the loaded Julia objects are structs that contain all settings characterizing a specific experiment. b) "output.csv" files that contain the final experimental outputs: estimated counterfactual evaluation metrics groups by model and counterfactual explainer.
- MIT
Reference papers
Mentions
- 1.Author(s): Giovanni De Toni, Stefano Teso, Bruno Lepri, Andrea PasseriniPublished in Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency by ACM in 2025, page: 89-10710.1145/3715275.3732008
- 2.Author(s): Gosia Migut, Aleksander Buszydlik, Mathijs M. de WeerdtPublished in Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1 by ACM in 2025, page: 410-41610.1145/3724363.3729101
- 3.Author(s): João Fonseca, Andrew Bell, Carlo Abrate, Francesco Bonchi, Julia StoyanovichPublished in Equity and Access in Algorithms, Mechanisms, and Optimization by ACM in 2023, page: 1-1110.1145/3617694.3623251